Resilience and prenatal mental health in Pakistan: a qualitative inquiry
Bibliographic record
Abstract
BACKGROUND: Women in Pakistan suffer from a high rate of depression. The stress of low-income, illiteracy, exposure to violence and living in a patriarchal society are predisposing vulnerabilities for depression, particularly during and following pregnancy. The resilience of an individual plays a significant role in promoting prenatal mental health, but this has yet to be thoroughly researched. In this article, our objective is to identify the core characteristics of resilience among pregnant women, which will then help us in developing an intervention. METHODS: The exploratory-descriptive study was conducted over 6 months in five different antenatal hospitals in Sindh, Pakistan. A total of 17 semi-structured interviews were conducted with pregnant women, purposefully selected with heterogeneous characteristics to explore diverse perspectives, while symptoms of depression were quantified by the Edinburgh Postnatal Depression Scale before the interview. Verbatim transcriptions were coded openly and merged into categories and themes. RESULT: A total of six themes emerged from in-depth thematic analysis: 1) purpose of life, 2) dealing with emotions, 3) believing in yourself, 4) optimistic approach, 5) strengthening support and relationship and 6) spirituality and humanity. Women agreed that these characteristics could help them improve their mental health. CONCLUSION: In conclusion, these themes were the core components of pregnant women's resilience which ultimately could help to promote prenatal mental health. These pave a pathway towards developing culturally and contextually resilience interventions aimed at enhancing mental health of pregnant women which then may improve neonatal and family mental wellbeing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".